GCP Quickstart

This quickstart will guide you through deploying a simple stack on GCP using mlstacks. We'll be deploying a simple storage bucket. This is as simple and quick an example of how mlstacks works as it gets.

Prerequisites

First, install the mlstacks CLI:

pip install mlstacks

You'll need an active GCP account and project to get started. (If you don't have one, you can create one following these instructions. You will also need sufficient permissions to be able to create and destroy resources.

If you don't have Terraform or Helm installed, you should also install them.

Defining our stack

Then, create a file called quickstart_stack.yaml wherever you have access to the mlstacks tool. In this file, add the following:

spec_version: 1
spec_type: stack
name: "quickstart_stack"
provider: gcp
default_region: "europe-north1"
default_tags:
  deployed-by: "mlstacks"
components:
  - simple_component_gcs.yaml

This defines our stack using the mlstacks specification. We'll now define the component that we want to deploy in a separate file called simple_component_gcs.yaml:

spec_version: 1
spec_type: component
component_type: "artifact_store"
component_flavor: "gcp"
name: "quickstart_gcs_bucket"
provider: gcp
metadata:
  config:
    bucket_name: "quickstart_gcs_bucket"
    project_id: "<YOUR_GCP_PROJECT_ID_HERE>"
  tags:
    deployed-by: "mlstacks"
  region: "eu-north1"

Deploying our stack

Now, we can deploy our stack using the mlstacks CLI:

mlstacks deploy -f quickstart_stack.yaml

This will deploy our stack to GCP. It will also deploy/provision a GCS bucket (beginning with zenml-mlstacks-remote-state by default) which will be used as a remote state store and backend for your Terraform assets. This will happen first before the deployment of your stack. You can now check your GCP console to see that the stack (and remote state bucket) has been deployed.

Get stack outputs

You can get the outputs of your stack using the mlstacks CLI:

mlstacks output -f quickstart_stack.yaml

This will print out the outputs of your stack, which you can use in your pipelines.

Destroying our stack

Finally, we can destroy our stack (and the remote state GCS bucket) using the mlstacks CLI:

mlstacks destroy -f quickstart_stack.yaml

What next?

You can now try adding more components and deploying them to your cloud provider. You can also try deploying your stack to a different cloud provider.

Good luck! And if you have any questions, feel free to reach out to us on Slack